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I have read an article talking about binary clustering using Matrix factorization(see attached), but i would like to understand some optimization concepts:

- Is it reasonable to use a Frobenius norm in such optimization?
- What does it mean the centroid constraint: C^T*1=0, and why it is equivalent to K-means especially in this case (When rho is large)?
- Is there other constraints that could improve clustering optimization?
- Is there other optimization technique in place of DPLM(Discrete Proximal Linearized Minimization)?

Hi. I just want to let you know that you cause use latex on this site. – nbro – 2020-08-18T00:11:40.430

Could you link the whole article? – Tinu – 2020-08-18T07:40:27.347

https://www.semanticscholar.org/paper/Binary-Multi-View-Clustering-Zhang-Liu/eb22f2ced9139a122103a236eac564b1525c5d05 – user40370 – 2020-08-18T17:05:44.730